Overview

Dataset statistics

Number of variables9
Number of observations5058
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory355.8 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

AUXU500R is highly overall correlated with AUXU1K1R and 1 other fieldsHigh correlation
AUXU1K1R is highly overall correlated with AUXU500R and 2 other fieldsHigh correlation
AUXU1K2R is highly overall correlated with AUXU500R and 2 other fieldsHigh correlation
AUXU2KR is highly overall correlated with AUXU1K1R and 3 other fieldsHigh correlation
AUXU3KR is highly overall correlated with AUXU2KR and 3 other fieldsHigh correlation
AUXU4KR is highly overall correlated with AUXU2KR and 3 other fieldsHigh correlation
AUXU6KR is highly overall correlated with AUXU3KR and 2 other fieldsHigh correlation
AUXU8KR is highly overall correlated with AUXU3KR and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-04-10 09:21:59.416023
Analysis finished2023-04-10 09:22:11.513346
Duration12.1 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

SEQN
Real number (ℝ)

Distinct3260
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49814.04
Minimum57
Maximum93689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:11.607334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile6123.7
Q131280.25
median54141.5
Q367377.75
95-th percentile90938.45
Maximum93689
Range93632
Interquartile range (IQR)36097.5

Descriptive statistics

Standard deviation26043.909
Coefficient of variation (CV)0.52282267
Kurtosis-0.96621407
Mean49814.04
Median Absolute Deviation (MAD)18691
Skewness-0.061414081
Sum2.5195942 × 108
Variance6.7828521 × 108
MonotonicityNot monotonic
2023-04-10T17:22:11.731548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4120 2
 
< 0.1%
84615 2
 
< 0.1%
19916 2
 
< 0.1%
32202 2
 
< 0.1%
28100 2
 
< 0.1%
5571 2
 
< 0.1%
89538 2
 
< 0.1%
71103 2
 
< 0.1%
34233 2
 
< 0.1%
64946 2
 
< 0.1%
Other values (3250) 5038
99.6%
ValueCountFrequency (%)
57 1
< 0.1%
150 1
< 0.1%
162 2
< 0.1%
191 2
< 0.1%
193 1
< 0.1%
493 1
< 0.1%
505 1
< 0.1%
560 2
< 0.1%
570 1
< 0.1%
613 2
< 0.1%
ValueCountFrequency (%)
93689 1
< 0.1%
93685 2
< 0.1%
93643 1
< 0.1%
93633 2
< 0.1%
93631 2
< 0.1%
93567 2
< 0.1%
93560 1
< 0.1%
93558 1
< 0.1%
93520 2
< 0.1%
93512 2
< 0.1%

AUXU500R
Real number (ℝ)

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.948992
Minimum0
Maximum120
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:11.833076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median30
Q340
95-th percentile65
Maximum120
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.868782
Coefficient of variation (CV)0.51273987
Kurtosis1.9081383
Mean30.948992
Median Absolute Deviation (MAD)10
Skewness1.1941796
Sum156540
Variance251.81824
MonotonicityNot monotonic
2023-04-10T17:22:11.944356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20 798
15.8%
25 785
15.5%
30 750
14.8%
15 538
10.6%
35 506
10.0%
40 390
7.7%
10 264
 
5.2%
45 251
 
5.0%
50 187
 
3.7%
55 138
 
2.7%
Other values (13) 451
8.9%
ValueCountFrequency (%)
0 11
 
0.2%
5 70
 
1.4%
10 264
 
5.2%
15 538
10.6%
20 798
15.8%
25 785
15.5%
30 750
14.8%
35 506
10.0%
40 390
7.7%
45 251
 
5.0%
ValueCountFrequency (%)
120 1
 
< 0.1%
105 4
 
0.1%
100 7
 
0.1%
95 7
 
0.1%
90 11
 
0.2%
85 13
 
0.3%
80 24
 
0.5%
75 36
0.7%
70 77
1.5%
65 88
1.7%

AUXU1K1R
Real number (ℝ)

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.558719
Minimum0
Maximum120
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:12.045473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q125
median30
Q340
95-th percentile65
Maximum120
Range120
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.22745
Coefficient of variation (CV)0.4537554
Kurtosis1.7990186
Mean33.558719
Median Absolute Deviation (MAD)10
Skewness1.139477
Sum169740
Variance231.87523
MonotonicityNot monotonic
2023-04-10T17:22:12.154228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
25 854
16.9%
30 779
15.4%
35 676
13.4%
20 650
12.9%
40 453
9.0%
15 385
7.6%
45 310
 
6.1%
50 218
 
4.3%
55 174
 
3.4%
60 147
 
2.9%
Other values (14) 412
8.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
5 22
 
0.4%
10 118
 
2.3%
15 385
7.6%
20 650
12.9%
25 854
16.9%
30 779
15.4%
35 676
13.4%
40 453
9.0%
45 310
 
6.1%
ValueCountFrequency (%)
120 2
 
< 0.1%
110 2
 
< 0.1%
105 1
 
< 0.1%
100 1
 
< 0.1%
95 9
 
0.2%
90 13
 
0.3%
85 16
 
0.3%
80 25
0.5%
75 46
0.9%
70 59
1.2%

AUXU1K2R
Real number (ℝ)

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.407473
Minimum0
Maximum120
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:12.253977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q125
median30
Q340
95-th percentile65
Maximum120
Range120
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.440901
Coefficient of variation (CV)0.46219902
Kurtosis1.7516126
Mean33.407473
Median Absolute Deviation (MAD)10
Skewness1.1415488
Sum168975
Variance238.42144
MonotonicityNot monotonic
2023-04-10T17:22:12.362963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
25 855
16.9%
30 801
15.8%
20 689
13.6%
35 617
12.2%
40 445
8.8%
15 361
7.1%
45 306
 
6.0%
50 218
 
4.3%
55 177
 
3.5%
60 135
 
2.7%
Other values (14) 454
9.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
5 34
 
0.7%
10 134
 
2.6%
15 361
7.1%
20 689
13.6%
25 855
16.9%
30 801
15.8%
35 617
12.2%
40 445
8.8%
45 306
 
6.0%
ValueCountFrequency (%)
120 2
 
< 0.1%
110 1
 
< 0.1%
105 1
 
< 0.1%
100 2
 
< 0.1%
95 12
 
0.2%
90 12
 
0.2%
85 15
 
0.3%
80 29
0.6%
75 46
0.9%
70 64
1.3%

AUXU2KR
Real number (ℝ)

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.163108
Minimum0
Maximum120
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:12.460621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q125
median40
Q350
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.165796
Coefficient of variation (CV)0.45230055
Kurtosis0.13168381
Mean40.163108
Median Absolute Deviation (MAD)15
Skewness0.55798334
Sum203145
Variance329.99613
MonotonicityNot monotonic
2023-04-10T17:22:12.573308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
30 610
12.1%
35 573
11.3%
40 510
10.1%
25 479
9.5%
45 423
8.4%
20 384
7.6%
50 370
7.3%
55 354
7.0%
60 276
 
5.5%
15 275
 
5.4%
Other values (15) 804
15.9%
ValueCountFrequency (%)
0 7
 
0.1%
5 35
 
0.7%
10 141
 
2.8%
15 275
5.4%
20 384
7.6%
25 479
9.5%
30 610
12.1%
35 573
11.3%
40 510
10.1%
45 423
8.4%
ValueCountFrequency (%)
120 1
 
< 0.1%
115 2
 
< 0.1%
110 2
 
< 0.1%
105 6
 
0.1%
100 12
 
0.2%
95 10
 
0.2%
90 16
 
0.3%
85 34
 
0.7%
80 71
1.4%
75 104
2.1%

AUXU3KR
Real number (ℝ)

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.553183
Minimum0
Maximum120
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:12.861723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q135
median50
Q360
95-th percentile80
Maximum120
Range120
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.907475
Coefficient of variation (CV)0.38155925
Kurtosis-0.15391916
Mean49.553183
Median Absolute Deviation (MAD)15
Skewness0.17341492
Sum250640
Variance357.49262
MonotonicityNot monotonic
2023-04-10T17:22:12.976313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
55 548
10.8%
60 486
9.6%
50 468
9.3%
40 428
8.5%
45 425
8.4%
35 417
8.2%
65 403
8.0%
30 388
7.7%
70 311
 
6.1%
25 268
 
5.3%
Other values (15) 916
18.1%
ValueCountFrequency (%)
0 6
 
0.1%
5 16
 
0.3%
10 47
 
0.9%
15 131
 
2.6%
20 174
3.4%
25 268
5.3%
30 388
7.7%
35 417
8.2%
40 428
8.5%
45 425
8.4%
ValueCountFrequency (%)
120 4
 
0.1%
115 5
 
0.1%
110 4
 
0.1%
105 8
 
0.2%
100 19
 
0.4%
95 34
 
0.7%
90 42
 
0.8%
85 89
1.8%
80 143
2.8%
75 194
3.8%

AUXU4KR
Real number (ℝ)

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.600435
Minimum-5
Maximum120
Zeros3
Zeros (%)0.1%
Negative1
Negative (%)< 0.1%
Memory size39.6 KiB
2023-04-10T17:22:13.078788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile25
Q145
median55
Q370
95-th percentile90
Maximum120
Range125
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.648229
Coefficient of variation (CV)0.34713918
Kurtosis-0.062642823
Mean56.600435
Median Absolute Deviation (MAD)15
Skewness0.064240393
Sum286285
Variance386.05289
MonotonicityNot monotonic
2023-04-10T17:22:13.190823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
65 543
10.7%
60 530
10.5%
55 524
10.4%
50 454
9.0%
70 408
 
8.1%
45 400
 
7.9%
40 334
 
6.6%
75 307
 
6.1%
35 269
 
5.3%
30 248
 
4.9%
Other values (16) 1041
20.6%
ValueCountFrequency (%)
-5 1
 
< 0.1%
0 3
 
0.1%
5 8
 
0.2%
10 30
 
0.6%
15 68
 
1.3%
20 126
 
2.5%
25 115
 
2.3%
30 248
4.9%
35 269
5.3%
40 334
6.6%
ValueCountFrequency (%)
120 9
 
0.2%
115 6
 
0.1%
110 16
 
0.3%
105 15
 
0.3%
100 67
 
1.3%
95 82
 
1.6%
90 125
2.5%
85 135
2.7%
80 235
4.6%
75 307
6.1%

AUXU6KR
Real number (ℝ)

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.825623
Minimum0
Maximum120
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2023-04-10T17:22:13.290993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q150
median65
Q380
95-th percentile100
Maximum120
Range120
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.49046
Coefficient of variation (CV)0.33670584
Kurtosis-0.26868382
Mean63.825623
Median Absolute Deviation (MAD)15
Skewness-0.094310217
Sum322830
Variance461.83987
MonotonicityNot monotonic
2023-04-10T17:22:13.402700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
70 508
 
10.0%
65 487
 
9.6%
60 444
 
8.8%
75 430
 
8.5%
55 398
 
7.9%
80 378
 
7.5%
50 313
 
6.2%
85 284
 
5.6%
45 267
 
5.3%
40 237
 
4.7%
Other values (15) 1312
25.9%
ValueCountFrequency (%)
0 5
 
0.1%
5 8
 
0.2%
10 17
 
0.3%
15 40
 
0.8%
20 74
 
1.5%
25 133
2.6%
30 141
2.8%
35 225
4.4%
40 237
4.7%
45 267
5.3%
ValueCountFrequency (%)
120 18
 
0.4%
115 31
 
0.6%
110 37
 
0.7%
105 80
 
1.6%
100 131
 
2.6%
95 163
 
3.2%
90 209
4.1%
85 284
5.6%
80 378
7.5%
75 430
8.5%

AUXU8KR
Real number (ℝ)

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.643733
Minimum-10
Maximum110
Zeros3
Zeros (%)0.1%
Negative3
Negative (%)0.1%
Memory size39.6 KiB
2023-04-10T17:22:13.503399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile25
Q155
median70
Q385
95-th percentile100
Maximum110
Range120
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.190218
Coefficient of variation (CV)0.32326648
Kurtosis-0.21106669
Mean68.643733
Median Absolute Deviation (MAD)15
Skewness-0.45467843
Sum347200
Variance492.40578
MonotonicityNot monotonic
2023-04-10T17:22:13.609143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
70 487
 
9.6%
75 461
 
9.1%
65 459
 
9.1%
80 424
 
8.4%
90 392
 
7.8%
85 389
 
7.7%
60 355
 
7.0%
55 286
 
5.7%
95 255
 
5.0%
50 241
 
4.8%
Other values (15) 1309
25.9%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-5 2
 
< 0.1%
0 3
 
0.1%
5 13
 
0.3%
10 16
 
0.3%
15 52
 
1.0%
20 79
1.6%
25 101
2.0%
30 114
2.3%
35 148
2.9%
ValueCountFrequency (%)
110 103
 
2.0%
105 127
 
2.5%
100 198
3.9%
95 255
5.0%
90 392
7.8%
85 389
7.7%
80 424
8.4%
75 461
9.1%
70 487
9.6%
65 459
9.1%

Interactions

2023-04-10T17:22:10.212902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:21:59.856682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.797117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.839070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.752083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:05.221952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.963380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.978981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.008172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.309547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:21:59.962116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.893096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.941433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.849069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:05.415539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.069863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.082342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.171426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.418157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.066526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.996529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.049782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.964220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:05.852210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.213743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.205244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.310397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.591184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.170159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.101695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.148782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:03.081988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.077353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.322724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.320192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.585271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.703480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.282549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.208162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.249382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:03.198406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.280904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.429908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.448446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.696713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.814674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.386217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.314746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.351647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:03.350352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.511582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.535894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.558669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.798486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.947582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.503435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.523782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.453217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:03.549628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.627806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.647474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.667215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:09.904041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:11.066604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.601690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.628085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.554501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:04.428889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.739795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.753904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.773698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.008386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:11.182171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:00.701262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:01.734405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:02.655485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:05.000103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:06.849085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:07.857466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:08.895439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2023-04-10T17:22:10.109025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2023-04-10T17:22:13.708645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
SEQNAUXU500RAUXU1K1RAUXU1K2RAUXU2KRAUXU3KRAUXU4KRAUXU6KRAUXU8KR
SEQN1.000-0.0310.0570.057-0.015-0.027-0.064-0.066-0.010
AUXU500R-0.0311.0000.6810.6910.3160.051-0.0520.0260.066
AUXU1K1R0.0570.6811.0000.9650.5470.1870.0440.0690.116
AUXU1K2R0.0570.6910.9651.0000.5530.1910.0490.0750.119
AUXU2KR-0.0150.3160.5470.5531.0000.6890.5240.4650.431
AUXU3KR-0.0270.0510.1870.1910.6891.0000.8530.7060.592
AUXU4KR-0.064-0.0520.0440.0490.5240.8531.0000.8030.669
AUXU6KR-0.0660.0260.0690.0750.4650.7060.8031.0000.830
AUXU8KR-0.0100.0660.1160.1190.4310.5920.6690.8301.000

Missing values

2023-04-10T17:22:11.338387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T17:22:11.457962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SEQNAUXU500RAUXU1K1RAUXU1K2RAUXU2KRAUXU3KRAUXU4KRAUXU6KRAUXU8KR
0574525203040455080
11621520205075807590
21912020202545609590
31933035302510102035
45054035354530403530
55605050557060757090
65702035352530355085
76132525252540454045
86553540404050707575
97315555555055556080
SEQNAUXU500RAUXU1K1RAUXU1K2RAUXU2KRAUXU3KRAUXU4KRAUXU6KRAUXU8KR
5048933642535303040456060
5049934541520204570758090
5050934982535353555503530
5051935123040403540352025
5052935202020204075758080
5053935582520203030557095
5054935675025251520252020
5055936313030304045556055
5056936333540403550353550
50579368515151535559090105